3 research outputs found
Applicability of Resilient Back-Propagation neural network for support for Design of Flux-Cored Wire
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μ κ°λ°νμλ€. μΈκ³΅ μ κ²½λ§μ 볡μ‘νκ³ λΉμ νμ μΈ λ¬Έμ λ₯Ό ν΄κ²°νλ λ§€μ° κ°λ ₯ν λꡬμ΄λ€. κ·ΈλΌμλ μ£Όμ΄μ§ λͺ©μ μ μν΄ μ΅μ μ μΈκ³΅μ κ²½λ§ λͺ¨λΈμ μ°Ύλ λ°©λ²μ μμ§ μλ €μ§μ§ μμκΈ° λλ¬Έμ μ μ ν μΈκ³΅μ κ²½λ§ λͺ¨λΈμ μ»λλ° μ΄λ €μμ΄ μλ€. λ°λΌμ λ§μ μνμ°©μ€μ μ μ ν μΈκ³΅μ κ²½λ§ λͺ¨λΈμ μ»λλ° λ§μ κΈ°κ°μ΄ νμνλ€. λ³Έ μ°κ΅¬μμλ νλ ₯μ μ€λ₯μμ ν μκ³ λ¦¬μ¦κ³Ό λ°μ΄ν°λ² μ΄μ€λ₯Ό κ²°ν©νμ¬ νλ‘κ·Έλ¨μ μ μ©νμκ³ λ°μ΄ν°λ² μ΄μ€μ κ²°ν©μ ν΅ν΄ μΈκ³΅μ κ²½λ§ λͺ¨λΈμ μ½κ³ λΉ λ₯΄κ² μμ±νμ¬ ν
μ€νΈν μ μλ€. νλ ₯μ μ€λ₯μμ ν μκ³ λ¦¬μ¦μ λ§€μ° λΉ λ₯Έ νμ΅ μκ³ λ¦¬μ¦μ΄κ³ κΈ°μ‘΄μ μΌλ°νλ λΈν κ·μΉκ³Ό κ°μ μμ ν μκ³ λ¦¬μ¦λ³΄λ€ μλ°± λ°° λΉ λ₯Έ νμ΅κ²°κ³Όλ₯Ό 보μ¬μ€λ€. |The development of a new filler material having the required properties is a very complicated work. A filler material contains many kinds of chemical components. The properties of the weld deposited are determined by the chemical and metallurgical reaction of these components. It is nearly impossible to quantitatively analyze this process due to their highly complex interactions. Therefore the design of a filler material has been carried out up to now on the basis of fundamental metallurgical knowledge and experiences of filler material designers. The development of a filler material usually requires a lot of tests and analyses for many pilot samples. This research aims to develop the estimation system of the properties of a filler material for reducing the amount of these tests and analyses in developing a filler material. In this paper, an estimation system using an artificial neural network(ANN) and database was developed to analyze and predict the workability and the deposited metal composition of a flux cored wire.
The neural network system is a very powerful tool to solve the complex and nonlinear problems. Nevertheless, it has a difficulty in obtaining an appropriate ANN model because the method to find optimal ANN model for any given purpose is not known yet. Therefore, it requires many trial and errors and much time to get the suitable ANN model. In this paper, the resilient backpropagation algorithm and database(DB)-coupled ANN system were applied. The resilient backpropagation algorithm is a very fast learning algorithm and shows the learning result several hundred times faster than the conventional backpropagation algorithm. The DB-coupled system can make many different ANN models and test easily and rapidly.1. μ λ‘
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Reference 48Maste